Recent advancements in diabetes technology include two parallel rapidly evolving areas: insulin delivery devices (subcutaneous or implanted insulin pumps) and continuous glucose monitors (CGM) recording frequent glucose determinations. So far, these two types of devices have not been linked successfully in a closed-loop glucose control system, e.g. artificial pancreas, which has the potential to dramatically improve blood glucose (BG) control, advance the quality of diabetes care, and help prevent costly complications of diabetes. Arguably, a minimally-invasive subcutaneous, SC-SC closed loop would have greatest potential for everyday use. Currently, there are five available SC CGM devices and several SC insulin pumps. A major challenge to a reliable external closed-loop control based on CGM and SC insulin injection remains the development of optimal control algorithms. A major obstacle to optimal control are two time delays inherent with SC systems: (i) the CGM resides in interstitial fluid and there exists a 5-20 minute time lag due to blood-to-interstitial glucose transport and sensor limits, and (ii) a change in the rate of insulin delivery takes ˜30 minutes to result in change in insulin action. While these time delays have little impact in steady metabolic states (e.g. during sleep), they are critical during rapidly changing metabolic demands, such as meals and physical activity.
Physical activity is recognized as a major trigger of potentially dangerous hypoglycemia. While patients may ingest glucose to compensate for acutely higher demand during exercise, the long-term (several hours) increase in insulin sensitivity attributed to exercise typically remains hidden. In addition, in automated closed loop, both the acute and long-term effects of physical activity will need to be handled without assistance. However, physical activity cannot be reliably detected via glucose monitoring alone because counterregulatory and other processes delay the BG fall. As a result, in most instances, a control algorithm relying on CGM data alone, would fail to reduce the insulin infusion in a timely way and would risk inducing hypoglycemia.
An additional input beyond BG is needed to detect the onset and magnitude of physical activity. A logical candidate for such a data input is heart rate (HR). Thus, the technology proposed regarding various aspects of the present invention meets an important need and provides the capability to overcome a major obstacle to closed-loop control—the inability to account for metabolic changes due to physical activity—by providing an additional information source through HR analysis.
During the past 10 years we have developed an array of mathematical methods describing the pathophysiology of Type 1 and Type 2 diabetes (T1DM, T2DM) at several system levels, from glucose-insulin control network to self-treatment behavior. Recently we have established collaboration with Prof. Claudio Cobelli, University of Padova, Italy, who has long-standing high visibility in the field of modeling glucose dynamics and is one of the authors of the now classic Glucose Minimal Model of Glucose Kinetics (MMGK).
Aspects associated with various embodiments of the present invention achieves, but not limited thereto, the following method, system and computer program product having the following objective: quantitatively describe the effects of physical activity on glucose-insulin dynamics in T1DM and develop an algorithm detecting via heart rate analysis the short-term and long-term changes in insulin sensitivity resulting from exercise. The method, system and computer program product may utilize the proposed algorithm that would have applications in both open-loop control systems providing feedback about metabolic state to the patient, and in fully automated closed-loop artificial pancreas.
Insulin Sensitivity:
The dynamics of interstitial concentrations of insulin and glucose has been mathematically characterized by Bergman and Cobelli's now classic MMGK [2],[3], and in a number of subsequent studies [4]-[10]. As a result, excellent methods exist for quantitative assessment of insulin sensitivity in a laboratory [4] and in an outpatient setting from oral glucose tolerance test (OGTT) [7]. The MMGK allows estimation of insulin sensitivity (SI) and insulin action (X) from intravenous tests (FIG. 1). Dr. Cobelli's group has been at the forefront of these investigations, with more than 200 publications addressing various aspects of glucose-insulin dynamics in health and disease, including estimates of postprandial glucose dynamics [13]-[17]. Usually the model is numerically identified by nonlinear least squares or maximum likelihood methods, however more sophisticated approaches in healthy and T2DM subjects have been used as well [19],[20]. The potential for adding a glucose tracer allowing the segregation of insulin action on periphery vs. the liver, has been investigated as well [20].
The MMGK is designed to mimic physiology via two ordinary differential equations: one governing the dynamics of glucose (considered to be a unique compartment G), another governing the dynamics of remote insulin action (compartment X). In these equations (presented below) SG represents the balance between liver production/clearance and insulin independent utilization, linearized around a basal glucose value Gb; X represents the insulin dependent glucose clearance; and Ra(t) the external input of glucose (meal or IV injection). The insulin dependent clearance is also a linear simplification around the basal insulin level Ib, and insulin sensitivity is defined as gain of the second equation:
      S    I    =                    p        3                    p        2              .  
                    {                                                                              G                  .                                =                                                      -                                                                  S                        G                                            ⁡                                              (                                                  G                          -                                                      G                            b                                                                          )                                                                              -                                      X                    ·                    G                                    +                                                            Ra                      ⁡                                              (                        t                        )                                                              V                                                                                                                                            X                  .                                =                                                                            -                                              p                        2                                                              ⁢                    X                                    +                                                            p                      3                                        ⁡                                          (                                              I                        -                                                  I                          b                                                                    )                                                                                                                              Eq        .                                  ⁢        1.1            
Effect of exercise on glucose homeostasis: Optimal meal management requires the injection, in a timely fashion, of enough insulin to return to target blood glucose value within minimum time, avoiding hypoglycemia. The challenge with physical activity is different in that we are not reacting to a system perturbation (such as glucose entering the blood via the GI track) but to transient changes in the parameters of glucose/insulin dynamics, which lead to increased effectiveness of insulin [21], and potentially to hypoglycemia. These changes are well known, though not always precisely quantified, and revolve mostly around changes in glucose transport through the cell membrane and vascular changes (FIG. 2). Exercise has been shown to augment the availability of the glucose transporter GLUT-4, both by translocation to the cell membrane [22]-[24] and increased transcription in muscle cells [25],[26]. These changes have been shown to be associated with an increase in insulin sensitivity and insulin independent glucose uptake [21],[24],[27],[28]. The pathways of exercise-induced translocation and augmented transcription are not entirely elucidated yet; but muscle fibers contractions have been proven to be at the source of these changes [28]. Though abundantly studied, the effects of exercise on glucose/insulin dynamics have been primarily approached in medical and biological terms. Concepts such as glucose transporter translocation, insulin sensitivity increase, or changes in transcription of transporters have been shown but never with a quantitative approach in mind. It is not to say that models have not been used to study these phenomena—there are numerous examples in the literature of studies using different versions of the MMGK to compare the glucose dynamics pre and post exercise [21],[27],[29]-[33]. However, real-time detection of the short- and long-term effects of physical activity on insulin sensitivity has not been accomplished.
Heart rate is a natural marker of physical activity due to its availability in the field and strong link with exercise duration and intensity [37]. Other metrics could be better suited to measure exercise intensity (e.g. VO2max and lactate threshold) and are tightly related to qualitative change in exercise physiology [38], but they are difficult to measure in field conditions. Considering the strong linear relationship displayed between maximum heart rate and VO2max [39], the proposed invention uses the difference between HR and a basal measure (minimum HR at rest) as a marker of exercise.